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Normative data for chronic pain questionnaires are essential to the interpretation of aggregate scores on these questionnaires, for both clinical trials and clinical practice. In this study, we summarised data from 13,343 heterogeneous patients on several commonly used pain questionnaires that were routinely collected from 36 pain clinics in Australia and New Zealand as part of the electronic Persistent Pain Outcomes Collaboration (ePPOC) including the Brief Pain Inventory (BPI); the Depression Anxiety and Stress Scales (DASS); the Pain Self-Efficacy Questionnaire (PSEQ); and the Pain Catastrophizing Scale (PCS). The data are presented as summarised normative data, broken down by demographic (age, sex, work status, etc) and pain site/medical variables. The mean BPI severity score was 6.4 (moderate-severe), and mean interference score was 7.0. The mean DASS depression score was 20.2 (moderate-severe), mean DASS anxiety was 14.0 (moderate), and mean DASS stress was 21.0 (moderate). The mean PCS scores were 10.0, 5.9, 14.1, and 29.8 for rumination, magnification, helplessness, and total, respectively. The mean PSEQ score was 20.7. Men had slightly worse scores than women on some scales. Scores tended to worsen with age until 31 to 50 years, after which they improved. Scores were worse for those who had a greater number of pain sites, were unemployed, were injury compensation cases, or whose triggering event was a motor vehicle accident or injury at work or home. These results and comparisons with data on the same measures from other countries, as well as their uses in both clinical practice and clinical trials, are discussed.

This article describes normative data on commonly used self-report questionnaires with a large sample of Australasian chronic pain clinic patients.

aFaculty of Medicine and Health, Pain Management Research Institute, University of Sydney and Royal North Shore Hospital, Sydney, New South Wales, Australia

bAustralian Health Services Research Institute, University of Wollongong, Wollongong, New South Wales, Australia

cSchool of Psychology, University of Shahed, Tehran, Iran

dFaculty of Medicine and Health, Centre for Education and Research in Ageing (CERA), University of Sydney, New South Wales, Australia

Sponsorships or competing interests that may be relevant to content are disclosed at the end of this article.

Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's Web site (www.painjournalonline.com).

1. Introduction

The availability of normative data for widely used questionnaires in the assessment of chronic pain is essential to the interpretation of scores on these questionnaires.13,28 Aggregate scores from questionnaires typically have little intrinsic meaning because they are assessed on arbitrary metrics. In clinical trials, this is less of a problem because scores are compared between groups, but in other study designs and in clinical practice, normative data are useful because they provide a comparator for sample or individual data and are particularly useful if they are broken down by key demographic, medical condition, and treatment variables. In summary, normative data act as a standardised comparator against which data collected in a variety of contexts can be compared.

Increasingly, large clinical, standardised databases are emerging and have many uses.4,8 For example, Quebec Pain Registry (quebecpainregistry.com) has been used in several studies, including instrument validation32 and predictive5 studies. Other examples are the Collaborative Health Outcomes Information Registry24 and the New York City Tri-Institutional Chronic Pain Registry.17 In Australia and New Zealand, a new initiative—the electronic Persistent Pain Outcomes Collaboration (ePPOC)—is becoming a valuable resource for pain researchers and clinicians. ePPOC was established in 2013 to evaluate and assist in improving outcomes and services for people experiencing chronic pain. Under ePPOC, specialist pain management services agree to collect standardised information about their patients and the services they provide. This information is used within each pain service to assess and monitor patients and is also submitted to a central coordinating site for analysis, reporting, and benchmarking purposes. Most of the specialist pain management services in Australia are now participating in ePPOC, with a number of services in New Zealand also joining the collaboration. At the time of writing, 50 adult pain services representing urban and regional locations, and publicly and privately funded units, had either joined or were in the process of implementing ePPOC.

The standardised assessment tools collected under ePPOC are the Brief Pain Inventory (BPI),3 the Depression Anxiety and Stress Scales (DASS), the Pain Self-Efficacy Questionnaire (PSEQ), and the Pain Catastrophizing Scale (PCS).26 Nicholas et al.19 reported normative data for several questionnaires commonly used to assess pain and related constructs, including the PSEQ and DASS. They presented descriptive statistics for around 5000 patients from one Australian centre, including means for each scale and subscale of these questionnaires broken down by sex, age group, and pain site. However, no large scale normative data have been provided for the BPI, and only limited Dutch29 and Canadian25normative data for the PCS are available. Therefore, there is a need to provide up-to-date normative data from a large and diverse sample of chronic pain patients for these 4 instruments.

The aim of this study is to use the ePPOC data set to collate and summarise normative data, broken down by demographic and pain site/medical variables, for these questionnaires, using a large sample of individuals who have been referred to pain clinics in Australia and New Zealand. We will compare these data to the previous Australian normative data19 and to the data available from other countries.

2. Methods

2.1. Participants

Thirty-six specialist adult pain services provided data for this study, 35 located in Australia and 1 in New Zealand. Baseline data (ie, initial pretreatment questionnaires) were submitted for 13,343 patients who attended a pain clinic between July 2013 and February 2016. Although the specific treatment received by each participant varied both within and between clinics, this heterogeneity has no bearing on the present data because all questionnaires were completed before treatment. Ethics approval was obtained from the University of Wollongong Human Research Ethics Committee (2015/501).

2.2. Instruments

The following assessment instruments were administered through article or electronic questionnaires, to patients in pain clinics as part of the ePPOC initiative, and data from these questionnaires were used in this study:

(1) Brief Pain Inventory3: The BPI is a 11-item instrument designed to assess the severity of pain and the extent to which it interferes with various daily activities. The severity subscale comprises 4 items assessing the following: (1) pain at its worst in the last week; (2) pain at its least in the last week; (3) pain on average; and (4) pain right now. Each of these items is scored 0 (“no pain”) to 10 (“pain as bad as you can imagine”). The severity subscale score is the mean of these 4 items, where a higher score represents greater pain severity. The interference subscale comprises 7 items, each scored 0 (“does not interfere”) to 10 (“completely interferes”). The interference subscale score is the mean of these 7 items, where a higher score represent greater interference with daily activities. There is evidence for the validity and reliability of the BPI in several patient groups and languages.3,9,21

(2) Depression Anxiety Stress Scales short form (DASS-21)16: The DASS-21 is a 21-item instrument designed to assess depression, anxiety, and stress. Each of these domains is represented by 7 items, where participants rate each item on a 0 (“did not apply to me at all”) to 3 (“applied to me very much, or most of the time”) scale. Each domain is represented by a subscale score, which is the sum of the item responses for that subscale multiplied by 2 to produce scores comparable with the original 42-item DASS (range 0-42). A higher score represents worse depression, anxiety, or stress. There is evidence for the validity and reliability of the DASS-21.12,31 Cut points for classification of scores as normal, mild, moderate, severe, and extremely severe, based on population norms, are provided in the DASS manual.16

(3) Pain Self-Efficacy Questionnaire18: The PSEQ is a 10-item instrument designed to assess the strength and generality of a patient's beliefs about his/her ability to accomplish various activities despite pain. Participants rate each item on a 0 (“not at all confident”) to 6 (“completely confident”) scale. Item scores are summed to provide a score with a possible range of 0 to 60, where higher scores indicate stronger self-efficacy. The PSEQ has strong psychometric properties.2,18,30

(4) Pain Catastrophizing Scale26: The PCS is a 13-item instrument assessing 3 domains of catastrophising: rumination (4 items); magnification (3 items); and helplessness (6 items). Participants rate each item on a 0 (“not at all”) to 4 (“all the time”) scale. A total PCS score is calculated as the sum of the items responses (range 0-52), where a higher score represents greater catastrophising. Each of the 3 domains is represented by a subscale score, which is the sum of the item responses for that subscale and where a higher scorer represents a higher level of rumination, magnification, or helplessness. The PCS has strong psychometric properties.20,26

In the standardised survey, the questionnaires were presented in the order specified above.

2.3. Statistical methods

Before analysis, the data set was screened for missing data. The ePPOC guidelines are to calculate aggregates for each subscale only if a certain number of items are having nonmissing data. For the BPI severity subscale, all items must be completed, whereas for the interference sub-scale, at least 4 of the 7 items must be completed. For the 3 DASS subscales, at least 6 of the 7 items must be completed. For the PSEQ, at least 9 of the 10 items must be completed. For the PCS total score, at least 12 of the 13 items must be completed, and for each PCS subscale, all items must be completed.

Mean values and SDs were calculated for the following: BPI severity subscale score; BPI interference subscale score; DASS depression subscale score; DASS anxiety subscale score; DASS stress subscale score; PCS rumination subscale score; PCS magnification subscale score; PCS helplessness subscale score; PCS total score; and PSEQ total score. These mean values and SDs were calculated for the total sample, as well as broken down by key variables: sex (male or female); age category (less than or equal to 20, 21-30, 31-40, 41-50, 51-60, 61-70, 71-80, or 81 years or more); primary pain site (head/face, neck, shoulder/upper limbs, back/spine/sacrum, lower limbs, or other); number of pain sites (based on the aforementioned categories); work status; whether or not a compensation case; and triggering event (injury at home, injury at work/school, injury in another setting, after surgery, motor vehicle crash, related to cancer, related to illness other than cancer, no obvious cause, or other). The pain site classification was based on the primary pain site nominated on the body map in the BPI and combined into the categories described above.

We also report Pearson correlations between each total and/or subscale score of each instrument, which may be useful for future analyses, including meta-analysis. All analyses were conducted in SPSS version 22. We chose not to analyse the data using significance testing because the large sample would have resulted in essentially trivial differences being statistically significant and not clinically meaningful.

3. Results

Table 3 shows the mean values, SDs, medians, and interquartile ranges for each of the subscale and total scores, for the total sample and broken down by sex. For the total sample, the mean BPI severity score was 6.4 (where 6 is considered “moderate” and 7 “severe”), and mean interference score was 7.0. Although Nicholas et al.19 did not administer the BPI, they included items asking about highest, lowest, and average pain on a 0 to 10 scale, which maps to 3 of the 4 items of the BPI severity scales, so it is worth comparing their results with the current data: in the present sample (compared with Nicholas et al.), worst pain was 8.0 (compared with 8.3), least pain was 4.8 (compared with 4.0), and average pain was 6.5 (compared with 6.4).

The mean DASS depression score was 20.2 (where 20 is considered the maximum score for classification as “moderate” and 21 the minimum for “severe”16), the mean DASS anxiety score was 14.0 (moderate-severe), and the mean DASS stress score was 21.0 (moderate). By contrast, the previously reported normative data2 for these measures the means were 14.3, 9.3, and 16.3, respectively.

The mean PCS subscale scores were 10.0, 5.9, 14.1, and 29.8 for rumination, magnification, helplessness, and total, respectively; the total mean score is just below the level of 30 classified as clinically relevant.25 The mean PSEQ score was 20.7, somewhat lower than the mean of 25.5 reported by Nicholas et al.19 Males had slightly worse scores (ie, lower for PSEQ) than women on some of the scores, but on others there was no difference.

The subsequent tables show the descriptive statistics for each of the subscale and total scores, broken down by each of the key variables. In general, there was little difference between these groups on pain severity and interference, but some differences emerged for scores on the other instruments. There was a trend in which the scores worsened with age (Table 4) until 31 to 40 or 41 to 50 years, after which they improved.

A breakdown of age groups by sex for each scale was prepared as separate table, but because of its large size, it has been provided as a supplementary table (Table S1, available at http://links.lww.com/PAIN/A743) because it may be useful for both clinicians and researchers.

For the sample as a whole, there were only small differences between the different primary pain sites on the different measures, apart from those with whole body pain who scored worse than all other groups on every measure (Table 5). Consistent with the last observation, when examining Table 6 (number of pain sites), it is evident that as the number of pain sites increased, scores tended to worsen. This pattern is reflected in the summary comparing 1 to 2 sites vs 3 to 6 sites.

Regarding work status (Table S2, http://links.lww.com/PAIN/A743), respondents who were unemployed, whether due to pain or not, tended to have worse scores than other respondents, and people who were employed (full or part time) had the best scores. Injury compensation cases (Table S3, http://links.lww.com/PAIN/A743) had worse scores than noncompensation cases. Finally, those whose triggering event (Table S4, http://links.lww.com/PAIN/A743) was a motor vehicle accident or injury at work or home had the worst scores, and those who attributed their pain to cancer had the best scores.

Table 7 displays percentiles for the total sample, and Table 8 displays the correlations between each of the subscale and total scores.

Table S5, http://links.lww.com/PAIN/A743, shows the amount and percentage of missing data for each item. In total, 9127 (68.4%) respondents had no missing data on any of the questionnaires, and 175 (1.3%) were missing all. The most common missing data pattern was missing the complete PCS and DASS questionnaires (198, 1.5%), followed by missing the complete PCS only (138, 1.0%). This is likely due to one pain management service inadvertently omitting the PCS and DASS from the standard questionnaire during the early stages of implementation of ePPOC data collection. The next most common missing data patterns (all < 1%) involved missing single individual items, mostly from the DASS but also from the PCS and BPI. The PSEQ had the least missing data (additional data on this available from the corresponding author).

4. Discussion

Routinely collected clinical databases are used increasingly in research and can be broadly classified as administrative databases and clinical (disease-based or treatment-based) databases.4 We have reported normative data for 4 questionnaires that are commonly used in pain management, obtained from a sample of approximately 13,000 patients from pain clinics in Australia and New Zealand. These data update and extend those reported by Nicholas et al.19 and use a much larger sample of patients from multiple clinics (vs just one), and include the questionnaires required as part of an Australian and New Zealand health services initiative (ePPOC). The low proportion of missing responses indicates the measures are broadly acceptable to pain patients, and that the calculated norms are based on valid and reliable data.

It is noteworthy that there was little difference in pain severity and interference scores between the different groupings, but some differences were found for the mood (DASS) and cognitive (PSEQ and PCS) questionnaires. Most notably, scores tended to worsen and then improve with older age, to worsen with a greater number of pain sites, and to be worse for people who were unemployed and/or injury compensation cases. Also worth noting are the worse scores for those whose pain was attributed to an accident or injury (many of whom were covered by compensation insurance) and the better outcomes reported by those reporting cancer-related pain. It should be noted, however, that the proportion reporting cancer-related pain was relatively small (n = 222, 1.1% of cases) and unlikely to be representative of those in the more acute and terminal stages of their illness, but nevertheless, they had been referred to a pain clinic that suggests pain was a recurring problem. It should also be emphasised that the relationships between the questionnaires and demographic characteristics are associations, and no causal inferences can be made.

Despite these caveats, the large size of this data set and the widespread source of the data indicate these data can be used to interpret an individual's score and the mean of a sample of individuals for each subscale of the 4 questionnaires reported, taking into account information about sex, age, or pain sites. The availability of these data will facilitate interpretation of scores on these instruments, in both research and clinical contexts (for discussion see Ref. 28). Nicholas et al.19 highlighted the utility of normative data using examples of clinical application of such data for individual patients. Such data can also be useful in gauging the characteristics of samples used in clinical research, especially when the same measures are employed. For example, Gardner et al.11 used the DASS and PSEQ (among other measures) to evaluate the use of goal setting by people with chronic low-back pain. According to the normative data presented here, the mean scores on these scales would have placed them in the normal (nondepressed mood) range on the DASS (5-10th percentile) and at very high pain self-efficacy (90th percentile) before treatment started. This is clearly a different sample to most of those attending pain clinics in Australia and New Zealand and needs to be borne in mind when drawing conclusions from that study.

On a broader level, the most recent Cochrane review of psychological therapies for chronic pain33 concluded that rather than more general trials of treatments, what was needed was studies that could “identify which components of CBT work for which type of patient on which outcome/s”. However, examination of the studies included in the review itself reveals considerable variation in the outcome measures used, most of which had no normative comparators, indicating a problem in addressing the questions posed by those researchers.

Compared with the normative data reported by Nicholas et al.,19 the present sample has lower pain self-efficacy and higher depression, anxiety, and stress. It is not immediately clear why this is the case because these data represent patients' experiences before receiving treatment in a pain management clinic, and the questionnaires are presented in standardised format and order across clinics. The 2 noteworthy differences between this sample and those previously reported (ie, sample size and number of clinics providing data) suggest the possibility that the single-clinic data were not broadly representative of the population of patients attending pain clinics in Australia and New Zealand. This highlights the need for normative data to be presented for large samples from a variety of sources. In this light, the relatively low representation of patients from New Zealand is a limitation of these data. As ePPOC is progressively implemented in pain management services throughout New Zealand more representative normative data can be reported for Australasia as a region.

Dworkin et al.8 highlighted the utility of standardised pain databases, and Cook and Collins4 articulated the many uses of large clinical databases. The ePPOC database is now used widely in Australia and New Zealand (over 70 clinics at present), so the normative data we report have greatest utility in these countries. In the absence of normative data from other countries, these norms can be used, but the possibility for there to be between-country differences in normative scores should be recognised. For these reasons, there is potential benefit in collecting country-specific normative data, and other countries should be encouraged to follow this example. Published mean scores on the Portuguese translations of DASS Depression scale and PSEQ for a heterogeneous sample of chronic pain patients (n = 311) attending Brazilian pain clinics, for example, were 14.03 (SD = 12.02) and 34.84 (14.08), respectively,22,23 which are noticeably better than the mean scores reported in this study and by Nicholas et al.19 A study of 121 chronic pain patients attending a pain clinic in Hong Kong,15 using published Chinese translations of PSEQ and PCS, a mean PSEQ score of 28.5 (SD: 13.3) and mean PCS score of 31.9 (SD: 11.1). In this case, mean self-efficacy was better than that reported in this study, but the catastrophising score was comparable. In Iran (based on 169 chronic pain patients), the mean PSEQ score was 36.01 (SD = 14.18),1 which mirrors the Brazilian findings. In the United States, a study of a heterogeneous sample of 427 chronic pain patients attending primary care clinics14 reported mean scores on the BPI severity and interference scales of 5.7 (SD: 1.8) and 5.8 (SD: 2.4), respectively. Both of these are lower (ie, better) than those reported in this study with pain clinic samples. This difference probably reflects expected differences in the nature of the populations served by pain clinics vs primary care.7 Similarly, a community-based Canadian chronic pain sample (n = 851) had a mean PCS of 20.90 (SD: 12.50),25 which is consistent with a nonpain clinic chronic pain population. Of course, the small samples mentioned above do not have the representative value of the ePPOC data set presented here, but they do illustrate the potential for using a large normative data set in the interpretation of studies conducted in different countries.

Another potential limitation is that the norms reported here are based on patient-rated assessment tools and questions and therefore only include data from those patients who are able to complete the measures. Individuals from non–English-speaking backgrounds or with low literacy levels will be underrepresented in these data. Finally, it is worth noting that about 3% of the sample had experienced pain for less than 3 months, which fails to satisfy the common criterion of chronic pain as having persisted for at least 3 months.27 Given the small percentage of such patients, this is not of great concern.

In summary, large standardised databases such as the ePPOC database have many uses, including the facilitation of interpretation of both group- and individual-level pain data by providing normative data for comparison across groups and centres, and between pain clinic treatment populations and those participating in clinical trials. One recent example that is topical is whether those people participating in online pain self-management programs are different (on these measures) to those attending pain clinics. Friesen et al.,10 for example, reported data from their online pain course with 60 diagnosed fibromyalgia patients (95% being female, with a mean age of 48 years). Looking at mean pretreatment scores for the treatment group, the BPI (pain) score was 5.4, vs 6.4 for the norms for people in the 40- to 50-year age group, and on the BPI (interference scale), the scores were 6.6 vs 7.3, respectively. On the PSEQ, it was 22.9 vs 18.9, respectively. These differences would suggest the online participants had slightly less severe pain and pain interference than the pain clinic samples. The online sample also reported slightly higher pain self-efficacy, which may make them more likely to accept a self-management approach. For example, Coughlan et al.6 found that chronic pain patients with mean initial PSEQ scores of 16.6 (SD:11.4) were significantly more likely to drop out an inpatient pain management program than those who completed the program (who had mean initial PSEQ scores of 24.4, SD: 11).

[22]. Sardá J Jr, Nicholas MK, Asghari A. The contribution of self-efficacy and depression to disability and work status in chronic pain patients: a comparison between Australian and Brazilian samples. Eur J Pain 2009;13:189–95.

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